Nonparametric System Identification Matlab Toolbox

Grzegorz Mzyk

2019

Abstract

In the paper the first version of Nonparametric System Identification Matlab Toolbox is presented. It is based on theoretical results concerning nonparametric identification method, achieved for the last four decades. The library includes both standard (kernel based or orthogonal expansion based) nonparametric methods and recent algorithms including combined (parametric-nonparametric) algorithms. Hammerstein and Wiener models and their serial connections are considered. Nonparametric estimates, usually run as a preliminary steps, play supporting role in the main procedure of estimating system parameters by the least squares method. Multi-level (hybrid) structure of algorithms, i.e. combining both parametric and nonparametric approaches allows to decompose the problem of identification of interconnected complex system into simpler local subproblems. Moreover, asymptotic consistency of all estimates was formally proved, even under existence of random and correlated noise.

Paper Citation

in Harvard Style

Mzyk G. (2019). Nonparametric System Identification Matlab Toolbox.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 691-698. DOI: 10.5220/0007922306910698

in Bibtex Style

@conference{icinco19,
author={Grzegorz Mzyk},
title={Nonparametric System Identification Matlab Toolbox},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={691-698},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007922306910698},
isbn={978-989-758-380-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Nonparametric System Identification Matlab Toolbox
SN - 978-989-758-380-3
AU - Mzyk G.
PY - 2019
SP - 691
EP - 698
DO - 10.5220/0007922306910698